Xiao Feng

The Industrial Internet of Things (IIoT) enhances industrial efficiency but also introduces substantial security challenges. Authentication is a key building block for securing IIoT networks. However, many recent IoT authentication schemes rely on offline registration and transmit temporary identity credentials in plaintext during registration, which exposes them to privileged-user attacks and limits their practicality in complex deployment scenarios. To address these issues, this paper presents an efficient three-factor authentication scheme with secure online registration for IIoT. The proposed scheme leverages Intel Software Guard Extensions (SGX) to protect the registration master key and support online registration. In addition, a dynamic credential update mechanism is introduced to mitigate privileged-user attacks. The security of the scheme is validated through ProVerif-based formal verification and informal security analysis, while its performance is evaluated through comparative analysis and NS-3 simulations. The results demonstrate that the proposed scheme provides enhanced security with low overhead, making it suitable for IIoT environments.

In the context of the diversity of smart terminals, the unity of the root of trust becomes complicated, which not only affects the efficiency of trust propagation, but also poses a challenge to the security of the whole system. In particular, the solidification of the root of trust in non-volatile memory (NVM) restricts the system’s dynamic updating capability, which is an obvious disadvantage in a rapidly changing security environment. To address this issue, this study proposes a novel approach to generate root security parameters using static random access memory (SRAM) physical unclonable functions (PUFs). SRAM PUFs, as a security primitive, show great potential in lightweight security solutions due to their inherent physical properties, low cost and scalability. However, the stability of SRAM PUFs in harsh environments is a key issue. These environmental conditions include extreme temperatures, high humidity, and strong electromagnetic radiation, all of which can affect the performance of SRAM PUFs. In order to ensure the stability of root safety parameters under these conditions, this study proposes an integrated approach that covers not only the acquisition of entropy sources, but also the implementation of algorithms and configuration management. In addition, this study develops a series of reliability-enhancing algorithms, including adaptive parameter selection, data preprocessing, auxiliary data generation, and error correction, which are essential for improving the performance of SRAM PUFs in harsh environments. Based on these techniques, this study establishes six types of secure parameter generation mechanisms, which not only improve the security of the system, but also enhance its adaptability in variable environments. Through a series of experiments, we verify the effectiveness of the proposed method. Under 10 different environmental conditions, our method is able to achieve full recovery of security data with an error rate of less than 25%, which proves the robustness and reliability of our method. These results not only provide strong evidence for the stability of SRAM PUFs in practical applications, but also provide a new direction for future research in the field of smart terminal security.